Lab Discussion

Large-scale biology projects such as the sequencing of the human genome and gene expression surveys using RNA-seq, microarrays and other technologies have created a wealth of data for biologists. However, the challenge facing scientists is analyzing and even accessing these data to extract useful information pertaining to the system being studied. This course focuses on employing existing bioinformatic resources – mainly web-based programs and databases – to access the wealth of data to answer questions relevant to the average biologist, and is highly hands-on.
Topics covered include multiple sequence alignments, phylogenetics, gene expression data analysis, and protein interaction networks, in two separate parts.
The first part, Bioinformatic Methods I, dealt with databases, Blast, multiple sequence alignments, phylogenetics, selection analysis and metagenomics.
This, the second part, Bioinformatic Methods II, will cover motif searching, protein-protein interactions, structural bioinformatics, gene expression data analysis, and cis-element predictions.
This pair of courses is useful to any student considering graduate school in the biological sciences, as well as students considering molecular medicine.
These courses are based on one taught at the University of Toronto to upper-level undergraduates who have some understanding of basic molecular biology. If you're not familiar with this, something like https://learn.saylor.org/course/bio101 might be helpful. No programming is required for this course although some command line work (though within a web browser) occurs in the 5th module.

從本節課中

Gene Expression Analysis II

When and where genes are expressed (active) in tissues or cells is one of the main determinants of what makes that tissue or cell the way it is, both in terms of morphology and in terms of response to external stimuli. Several different methods exist for generating gene expression levels for all of the genes in the genome in tissues or even at cell-type-specific resolution. In this class we'll be hierarchically clustering our significantly differentially expressed genes from last time using BioConductor and the built-in function of an online tool, called Expression Browser. Then we'll be using another online tool that uses a similarity metric, the Pearson correlation coefficient, to identify genes responding in a similar manner to our gene of interest, in this case AP3. We'll use a second tool, ATTED-II to corroborate our gene list. We'll also be exploring some online databases of gene expression and an online tool for doing a Gene Ontology enrichment analysis.